An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks

Rezvy, Shahadate ORCID: https://orcid.org/0000-0002-2684-7117, Luo, Yuan, Petridis, Miltos, Lasebae, Aboubaker and Zebin, Tahmina (2019) An efficient deep learning model for intrusion classification and prediction in 5G and IoT networks. In: 2019 53rd Annual Conference on Information Sciences and Systems (CISS), 20-22 Mar 2019, Baltimore, MD, USA, USA. (doi:10.1109/CISS.2019.8693059)

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Abstract

A Network Intrusion Detection System is a critical component of every internet-connected system due to likely attacks from both external and internal sources. Such Security systems are used to detect network born attacks such as flooding, denial of service attacks, malware, and twin-evil intruders that are operating within the system. Neural networks have become an increasingly popular solution for network intrusion detection. Their capability of learning complex patterns and behaviors make them a suitable solution for differentiating between normal traffic and network attacks. In this paper, we have applied a deep autoencoded dense neural network algorithm for detecting intrusion or attacks in 5G and IoT network. We evaluated the algorithm with the benchmark Aegean Wi-Fi Intrusion dataset. Our results showed an excellent performance with an overall detection accuracy of 99.9% for Flooding, Impersonation and Injection type of attacks. We also presented a comparison with recent approaches used in literature which showed a substantial improvement in terms of accuracy and speed of detection with the proposed algorithm.

Item Type: Conference or Workshop Item (Keynote)
Research Areas: A. > School of Science and Technology > Computer and Communications Engineering
Item ID: 26739
Notes on copyright: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Depositing User: Yuan Roger Luo
Date Deposited: 07 Jun 2019 13:32
Last Modified: 14 Jun 2019 16:45
ISBN: 9781728111513
URI: https://eprints.mdx.ac.uk/id/eprint/26739

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